Image processing is commonly used in the infrared industry for various purposes. For example, many infrared image processing routines typically rely on filters to extract particular aspects from an image frame. As a specific example, filters are frequently used to separate low-frequency and high-frequency components of an image from the image frame. This process may facilitate noise identification in both frequency regimes. These filters may also be useful for enhancing image quality by emphasizing (or diminishing) details in the image.
Many useful filters are calculated directly from image frame data. The raw frame data may be subjected to numerical manipulation according to the filter algorithm, with the end result being a filtered frame. The filtered frame may then be useful for testing or for image quality improvement. However, the process of calculating a filtered frame from the raw image may be vulnerable to spatial and temporal noise and to outlier pixels in the raw frame, a fact that is particularly evident with infrared images.
For example, raw infrared images may include spatial and temporal noise, with common noise manifestations including for example row and column noise, aperture warping (also known as cos4 fall-off), and/or flickering, slow, dead, railed, or odd pixels. These noise components can seriously degrade image quality and they can also create problems for filtering schemes. Noisy pixels may have an inordinate effect on neighboring pixels during the filtering process, which may lead to an undesirable result with conventional filtering schemes.
As a result, there is a need for improved filtering techniques for infrared image processing.